Method for measuring venous oxygen saturation

ABSTRACT

A method for measuring venous oxygen saturation levels has steps of measuring optical absorption oscillation data at the respiratory frequency at a plurality of wavelengths ( 2 ). A reduced scattering coefficient and an absorption coefficient are determined for the tissue, with the result that an effective path length can be determined ( 6 ). Data processing is performed to calculate amplitudes for the absorption oscillation data that are translated into oxygenated and deoxygenated hemoglobin concentrations for the venous compartment ( 8 ). A method of the invention does not required mechanical ventilation devices or venous perturbation. Additional method steps may entail verifying that the measured absorption oscillation data results from the venous compartment.

[0001] The present invention is related to apparatuses and methods thatutilize spectrometry to determine oxygen saturation levels of tissue.More particularly, the present invention is related to apparatuses andmethods that utilize near infrared spectrometry to non-invasivelydetermine oxygen saturation levels of venous compartments.

BACKGROUND ART

[0002] The use of spectrometry to measure tissue oxygen saturationlevels in a non-invasive manner is known in a general sense. Indeed,efforts to exploit the oxygenation dependent light absorption propertiesof hemoglobin to determine the oxygen saturation of hemoglobin in vivohave been made for several decades. In the 1980's so-called “pulseoximeters” became commercially available, and have continued to develop.An important advantage of such instruments is their ability to providecontinuous, safe, and effective monitoring of arterial oxygenationnon-invasively at a patient's bedside.

[0003] Pulse oximeters operate on the known principle that oxygenatedand deoxygenated hemoglobin show different absorption spectra.Deoxygenated hemoglobin absorbs more light in the red band (typically650-750 nm), while oxygenated hemoglobin absorbs more light in theinfrared band (typically 850-1000 nm). Pulse oximeters generally use onewavelength in the near infrared band and one in the red band to measurethe oxygen saturation of arterial blood. Traditionally, pulse oximeteryhas faced problems associated with determining the scatter that occursin tissue. Without such a determination, accurate measures of absorptionare not possible. Typically this problem has been addressed by“calibrating” devices on a population of healthy subjects to empiricallydetermine levels of scatter.

[0004] Recent advances in pulse oximetery have been made. For example,the introduction of time-resolved optical spectroscopy in conjunctionwith diffusion theory has lead to quantitative tissue spectroscopy, asdescribed in U.S. Pat. No. 5,497,769 to Gratton et al. (“the '769patent”), which is incorporated by reference herein. The '769 patentgenerally discloses an apparatus useful for measuring the hemoglobinsaturation in tissue that is particularly sensitive to blood in thecapillaries where oxygen is exchanged with the tissue. Thus the '769patent generally teaches an apparatus useful for measuring tissue oxygensaturation to give an indication of tissue oxygen consumption, but doesnot measure time-varying hemoglobin compartment saturation.

[0005] More recently, U.S. Pat. No. 6,216,021 to Franceschini et al.discloses a spectrometry-based method for the real time, non-invasive,simultaneous measurement of tissue hemoglobin saturation and timevarying arterial hemoglobin saturation. The '769 patent provides asolution to the tissue scatter problem. Generally, the light signal ismeasured at two locations to determine tissue optical properties. Theoptical properties determine the amount of scatter that occurs as lighttravels through tissue. To determine time varying hemoglobin compartmentsaturation, an amplitude of absorption oscillations at the frequency ofarterial pulsation is quantitatively calculated at multiple wavelengthsfrom the oscillations of the optical signal collected by thespectrometer using the determined tissue optical properties, with theresult that an absolute value of the arterial hemoglobin saturation maythen be determined. Determination and use of the tissue opticalproperties allows the method of the '021 patent to be used without priorcalibration of a spectrometer on a population of healthy subjects.

[0006] The teachings of the prior art, however, have generally beenineffective in regards to measurement of venous compartment saturationlevels. Experimental approaches have been proposed for suchmeasurements. For instance, the use of spectrometry in combination withphysical manipulation of a patient has been proposed. Physicalmanipulations associated with these methods include use of a venousocclusion on a limb, tilting a patient's head downward, use of a partialjugular vein occlusion, use of mechanical ventilation, and the like.Generally, these methods contemplate optically measuring the venoussaturation by measuring the increase in oxygenated hemoglobinconcentration and in the total hemoglobin concentration induced by thelocal increase in the venous blood volume. All of these proposedmethods, however, have significant disadvantages associated with them.Required physical manipulation of the patient, for instance, can becumbersome, painful, and at times impractical. Additionally, thesemethods may be limited to use on the limbs (e.g., venous occlusionmethods).

[0007] Additional unresolved problems in the art relate to verifying theaccuracy of venous compartment measurements. That is, the proposedmethods of the prior art generally have a considerable level ofuncertainty associated with them. This uncertainty results from the manydifficulties faced in isolating the venous compartment contribution tooptical tissue absorption measurements.

[0008] Unresolved needs in the art therefore exist.

DISCLOSURE OF THE INVENTION

[0009] The present invention is directed to methods and program productsfor non-invasively determining the venous oxygen saturation. Generally,these methods and program products are directed to optically measuringoscillations at the respiratory frequency of a subject and use a dataprocessing step to determine an amplitude of the absorptionoscillations. Using the oscillation amplitude data, a venous oxygenationsaturation level may be determined. Methods of the invention do notrequire that the subject be ventilated with mechanical ventilationdevices or the like, or that venous perturbations be used. It will beappreciated, however, that methods of the invention may be used undersuch circumstances.

[0010] Preferably, the optical measurements are performed at a pluralityof wavelengths. Data processing may comprise calculating an amplitude ateach of the plurality of wavelengths using particular processing steps.As examples, particular data processing may comprise performing aFourier transformation, or applying a band pass filter in combinationwith a modeling algorithm. A method of the invention also may comprisesteps for verifying the accuracy of venous saturation level measurement.

[0011] The present invention thereby resolves many otherwise unsolvedproblems in the art. For example, levels of venous oxygenation may beaccurately measured without requiring mechanical ventilation or venousperturbation of a subject. Additionally, embodiments of the inventionoffer novel steps that solve otherwise unresolved problems in the artrelated to verifying that measured absorption result from the venouscompartment.

BEST MODE OF CARRYING OUT THE INVENTION

[0012] Other features, objects and advantages of the invention will beapparent to those skilled in the art by reference to the detaileddescription in view of the drawings, of which:

[0013]FIG. 1 is a flowchart illustrating in general steps of aninvention method embodiment;

[0014] FIGS. 2(a)-(c) is a block diagram illustrating a spectrometerdevice useful for practice with an invention embodiment;

[0015]FIG. 3 is a block diagram illustrating a spectrometer deviceuseful for practice with an invention embodiment;

[0016]FIG. 4 is a flow chart illustrating example verification steps ofinvention embodiments; and

[0017] FIGS. 5(a)-(b) are graphs useful for illustrating measurementverification steps of an invention embodiment.

DETAILED DESCRIPTION

[0018] The present invention is directed to methods and program productsfor determining venous oxygen saturation levels through opticalmeasurements performed at the respiratory frequency. More particularly,the present invention is directed to methods and program productswhereby the amplitude of optical absorption oscillations are determinedat the respiratory frequency, with data processing then applied toconvert the absorption data into oxygenated and deoxygenated hemoglobinlevels.

[0019] Turning now to the drawings, FIG. 1 is a flowchart illustratingin general the steps of an embodiment of the method of the invention.The embodiment of FIG. 1 comprises a step of measuring opticalparameters for tissue comprising at least an absorption coefficientμ_(a) and a reduced scatter coefficient μ_(s)′ (block 2). Absorptionchanges are then measured over time at a plurality of wavelengths (block4), with at least 2 wavelengths required and at least 8 preferred. Usingthe optical parameters and the measured absorption changes over time,the amplitudes of absorption oscillations at the respiratory frequencyare then calculated (block 6). The spectrum of the experimentalabsorption amplitude is then fit with a hemoglobin absorption spectrumto determine an oxygen saturation of venous hemoglobin.

[0020] Referring now to FIGS. 2(a)-(c), an apparatus for practicingmethods of the invention comprises a frequency-domain spectrometer 10 toobtain time-resolved measurements of phase and amplitude. Artisans willappreciate that optical measurements can be performed with eithercontinuous wave methods, i.e., using constant light intensity, or withtime-resolved methods. Time-resolved methods include the time-domainwhere light intensity is pulsed with a pulse width in the order ofpicoseconds or less, and the frequency-domain, where light intensity issinusoidally modulated at a radio frequency. In the time-domain, thetime-of-flight distribution of detected photons is measured, whereas inthe frequency-domain an average intensity, the amplitude, and a phase ofa detected modulated intensity are measured. The time-domain and thefrequency-domain methods are mathematically related by a temporalFourier transform. Thus, the time-domain method is equivalent to acollection of frequency-domain measurements over a band of modulationfrequencies.

[0021] For exemplary purposes, an embodiment of the present invention isdescribed with the frequency-domain approach to measurement of venoushemoglobin saturation. However, the same method described is applicablein the time-domain. One aspect of the invention is the use of atime-resolved optical method, which is performed in either thefrequency-domain or the time-domain. Importantly, time-resolved methodsallow the separation of reduced scattering and absorption coefficientsof tissues. The separation of reduced scattering and absorptioncoefficients of tissues allows for absolute absorption measurements and,therefore, absolute hemoglobin concentration measurements.

[0022] Artisans will appreciate that in frequency-domain spectroscopythe intensity of the light source is modulated at a radio frequency f,preferably 110 MHz, and the detector sensitivity is modulated at afrequency f+Δf, where the offset frequency Δf is lower than f,preferably in the kHz range. It is noted, however, that radiofrequencies other than 110 MHz are viable, being limited only by thefact that too low a radio frequency results in inadequate phase shiftand too high a radio frequency will be outside a range of knowndetectors' capabilities.

[0023] The frequency-domain spectrometer 10 connects to an optical probe12 and a processor 14 for analyzing data from the frequency-domainspectrometer 10 and optical probe 12. Detector output is passed througha low-pass filter 16 to a fast Fourier transformer 18 to provide anaverage intensity (dc), an amplitude (ac), and a phase (Φ), i.e.,time-resolved measurement data, of the detected signal at frequency f.

[0024] The optical probe 12 preferably contains optical source fibers 20that guide light to tissue to be examined, and detector fibers 22 thatguide the collected light from tissue to an optical detector 24, such asa photo multiplier tube or photo sensor. The probe 12 should preferablybe lightweight and partially flexible to adapt to the surface of theexamined tissue, but its shape should remain substantially unaltered inorder to maintain a well defined and fixed geometrical relationshipbetween the source fibers 20 and detector fibers 22. The optical probe12 is designed to afford quantitative tissue spectroscopy withoutrequiring any sort of instrumental calibration. To position both thesource fibers 20 and detector fibers 22 of the optical probe 12 on thecommon side of the tissue sample, the invention method uses diffusedreflection geometry. This feature allows the optical probe 12 to beapplied to any tissue of interest.

[0025] To determine a geometrical arrangement of the source fibers 20and the detector fibers 22, the distance a should be greater than, or inthe order of, 1.0 cm to achieve a sufficient optical penetration depthinto the tissue. It is noted that this separation distance of about 1.0cm is less than distances that may be useful for performing arterialmeasurements (e.g., about 1.5 cm). It has been discovered that a loweroptical penetration depth is desirable to achieve a higher sensitivityto superficial veins, which is advantageous for measurement of thevenous saturation. In addition, a+b should be less than, or in the orderof, 4 cm to collect data with a high signal-to-noise ratio. Moreover, bshould assume values between about 0.5 cm and about 2 cm to be largeenough to distinguish different signals at separations a and a+b, andsmall enough to ensure that the signals at separations a and a+b probeessentially the same tissue volume.

[0026] By way of additional description useful for practice of methodsof the invention, reference is now drawn to FIG. 3 in addition to FIG.2. The preferred frequency-domain tissue spectrometer 10 includes afrequency-synthesizer 38 to modulate the intensity of laser diodes at afrequency of x MHz, e.g., 110 MHz. The frequency-synthesizer 38 alsomodulates a second dynode of two photo multiplier tubes (pmt), pmt a 24a and pmt b 24 b, of the optical detector 24 (FIG. 2), at a frequency ofy MHz, e.g., 110.005 MHz. An example of a suitable commerciallyavailable frequency domain spectrometer is Model No. 96208 availablefrom ISS Inc., Champaign, Ill. Artisans will appreciate that otheroptical detectors can be used, but pmt's are preferred for theirsensitivity.

[0027] The frequency-domain spectrometer 10 operates at at least twowavelengths in a range from about 600 to 1000 nm. For exemplarypurposes, eight discrete wavelengths n1-n8 (for example 636, 675, 691,752, 780, 788, 830, 840 nm) may be used in the red and near-infraredspectral region. Artisans will appreciate that other similar wavelengthsmay be used. It is noted that optical spectroscopy in the wavelengthrange from 600 to 1000 nm achieves a sufficient photon penetration depthto non-invasively probe macroscopic tissue volumes and remains sensitiveto oxygen saturation of hemoglobin.

[0028] A multiplexer 44 multiplexes the light sources, two laser diodescontained in a laser driver 46 per each wavelength, at a rate of z Hz,e.g., 100 Hz, to timeshare the two photo multiplier tubes. As a result,50 cross correlation periods are acquired during the on-time of eachlaser diode, and a complete acquisition cycle over the eight wavelengthsis completed every 80 ms (or 160 ms if two lasers per each wavelengthare employed). The multiplexer 44 electronically multiplexes the lightsources at a rate z such that N/z≧P/2, with N total number of lightsources, and P period of oscillation of the time-varying hemoglobincompartment to be measured. Although practice of an invention method maybe accomplished using as few as a single breathing cycle, preferablydata is taken over a plurality of breathing cycles, by way of exampleover about 10 or more cycles. 10-15 cycles has been discovered to be amost preferable range. Because different animals have differentbreathing cycles, this will result in different numbers of data pointsbeing obtained for study of different animals.

[0029] As an example, piglets have a breathing cycle of from 0.6-0.9 Hz,with the result that 10-15 cycles occur in a time period of about 20sec. At a rate of a complete spectrum being measured in 80 ms, then,about 256 data points are taken for piglets over the most preferred10-15 breathing cycle range. Further, it may be desirable to averagesuccessive FFT to obtain more meaningful data. For example, in a studyof piglets it may be desirable to average about 800 successive fastFourier transformations, with each computed from a data set shifted byone data point with respect to the previous one. Accordingly, a total ofabout 1,000 data points may be taken to correspond to one venoussaturation reading corresponding to a data train of 80 s in length.Humans have a lower breathing frequency of about 0.22-0.26 Hz, with aresult that about 40-60 seconds are required for 10-15 breathing cyclesto occur. At a rate of 80 ms for a spectrum to be measured, about 750data points are required. Again, it may be desirable to obtain thesedata points from averaging a larger number of consecutive fast Fouriertransformation points.

[0030] Each one of sixteen laser diodes is coupled to an optical fiberapproximately 400 μm in core diameter. The preferred embodiment groupsthe two sets of eight fibers guiding light at the eight wavelengths intotwo source fiber bundles 20 having a rectangular section of internalsize, for example, 3.5×1.2 mm². The optical signal detected on tissue isguided to two parallel detector channels of the spectrometer by twooptical detector fiber bundles 22, for example, 3 mm in internaldiameter. The source fibers 20 and the detector fibers 22 are placed onthe common side of the examined tissue, for example a forehead, in thesymmetrical configuration shown in FIG. 3.

[0031] As discussed above with reference to FIG. 2 above, thisgeometrical arrangement of the source fiber 20 and the detector fibers22 features four distinct source-detector pairs, and two distinctsource-detector separations (a and a+b, e.g., 1 cm and 2 cm,respectively). Artisans will appreciate that other distances between thesource and detector fibers are contemplated. The outputs of pmt a 24 aand pmt b 24 b are processed in the data processing apparatus 16, 18,14, as seen in FIG. 3, and the results are provided in a display 48.

[0032] This source-detector configuration affords quantitativespectroscopy independent of source, detector, and optical-couplingterms, i.e., without requiring instrumental calibration. To avoidpre-calibration, the present invention relies on a physical model toquantitatively describe the relationship between the collected opticalsignal and tissue optical properties. This model, which assumes amacroscopically uniform distribution of the time-varying hemoglobincompartment in tissue, is well justified in most cases, but there may becases where its assumptions are not fulfilled. To maximize thesignal-to-noise ratio, the calculation of μ_(a)′ may be regularlyupdated; e.g. every 10 seconds. In this way, the contribution of thephase noise to the measurement of μ_(a) is strongly reduced, whereas theacquisition time for the absorption spectrum is maintained.

[0033] Referring now to the process of data analysis, the processor 14operates so that a respiratory component of tissue absorption can bequantified to produce absolute values of time-varying hemoglobincompartment and/or tissue saturation. A first step of the analysisconsists of a quantitative determination of a tissue reduced scatteringcoefficient (μ_(s)′) and an absorption coefficient (μ_(a)) (block 26 inFIG. 2). To this aim, a multi-distance method is applied using two ormore source-detector separation distances (a and a+b). This is achievedby using multiple sources either multiplexed or modulated at differentfrequencies to electronically distinguish the corresponding signalsand/or multiple detectors which can acquire data in parallel, orsequentially. Additionally, this can be achieved by moving a source withrespect to a detector over the desired range of source-detectorseparation.

[0034] The multi-distance time-resolved measurement method assumes ahomogeneous and semi-infinite geometry. The absolute values of theabsorption (μ_(a)) and reduced scattering (μ_(s)′) coefficients oftissue are given in terms of the dc, ac, and phase slopes versussource-detector separation (S_(dc), S_(ac), and S_(Φ), respectively). Inparticular, either the S_(dc) and S_(Φ) pair, or the S_(ac) and S_(Φ)pair can be used to measure μ_(a) and μ_(s)′. Using dc and phase:$\mu_{s}^{\prime} = {{\frac{2\quad \upsilon}{3\quad \omega}{S\quad}_{\Phi}\left( {S_{\Phi}^{2} + S_{dc}^{2}} \right)^{\frac{1}{2}}\quad \mu_{a}} = \frac{S_{dc}^{2}}{3\quad \mu_{s}^{\prime}}}$

[0035] using ac and phase: $\begin{matrix}{\mu_{s}^{\prime} = {\frac{2\quad v}{3\quad \omega}{S\quad}_{\Phi \quad}S_{ac}}} & {\mu_{a} = {\frac{S_{ac}^{2}}{3\quad \mu_{s}^{\prime}}\left\lbrack {1 - \left( \frac{3\quad \omega \quad \mu_{s}^{\prime}}{2\quad {vS}_{ac}^{2}} \right)^{2}} \right\rbrack}}\end{matrix}$

[0036] where ω is the angular modulation frequency of the sourceintensity, and ν is the speed of light in tissue. More informationregarding the application of a multi-distance method may be had byreference to “Quantitative Determination of the Absorption Spectra ofChromophores in Strongly Scattering Media: a Light-Emitting-Diode BasedTechnique,” by Fantini et al., Appl. Opt. 33: 5204-5213, 1994; which isincorporated herein by reference.

[0037] The present method averages the μ_(s)′ measurement on a timescale T, which is longer than a data acquisition time t, where the dataacquisition time t is equal to or less than about one-half a period ofoscillation. For example, for a data acquisition time of t>250 ms, aμ_(s)′ measurement time T is approximately 5 seconds. The longer timescale allows the present invention to maintain a high temporalresolution in the absorption measurement, while drastically reducing thecontribution of the phase noise because the phase data only appears inthe expression for μ_(s)′ which is averaged over time T. The onlyassumption of this method is that the reduced scattering coefficientμ_(s)′ does not vary on a time scale faster than T, which is generallytrue for T in the order of a few seconds.

[0038] To measure tissue saturation Y, the spectrum of the tissueabsorption is fit with a linear combination of the extinction spectra ofoxygenated hemoglobin and deoxygenated hemoglobin. The fitted parametersare the concentrations of oxygenated hemoglobin ([HbO₂]) anddeoxygenated hemoglobin ([Hb]). Tissue saturation Y is then given by theexpression [HbO₂]_(t)/([HbO₂]_(t)+[Hb]_(t)), where the subscript “t”indicates that the involved concentrations refer to tissue (block 28).Since tissue saturation Y is determined by absorption μ_(a) and not byscattering μ_(s)′, the present invention achieves an absolutemeasurement of tissue saturation Y with a fast temporal resolution andhaving excellent signal-to-noise ratio characteristics.

[0039] In addition to the above measured tissue saturation Y, thetime-resolved measurements may be used to simultaneously measure thetime-varying hemoglobin venous compartment (SvO₂). The venous hemoglobinconcentration in tissue oscillates with time as a result of therespiration induced volume changes in the venous compartment. When asubject inspires the chest cavity is expanded as air enters and fillsthe lungs. This expansion is associated with a decrease in theintrathoracic pressure that in turn causes an increased venous returnbecause of the increased pressure gradient between peripheral andintrathoracic veins. As a result, the volume of hemoglobin in theperipheral venous compartment decreases. As the subject expires, thechest cavity contracts, the intrathoracic pressure is increased, venousreturn is decreased, and the peripheral venous compartment expands.Hemoglobin volume correspondingly increases in the peripheral venouscompartment. Consequently, the detected oscillations in the opticalsignal at the respiratory frequency can be assigned to the venoushemoglobin compartment. A venous compartment saturation can then berelated to the oscillatory components of the absorption coefficients attwo or more wavelengths.

[0040] To perform absolute venous oximetry using the time-resolvedmeasurements, the amplitude of the respiration-induced absorptionoscillations is quantitatively measured. Embodiments of the presentinvention comprise use of a modified Beer-Lambert law approach totranslate the temporal intensity ratio collected at each wavelength[I(λ,t)/I(λ,0)] at a known distance (e.g., 1 cm) from the illuminationpoint, into a time variation in the tissue absorption [Δμ_(a)(λ,t)].More information regarding the application of a modified Beer-Lambertlaw approach may be had by reference to “Estimation of OpticalPathlength through Tissue from Direct Time of Flight Measurement”, byDelpy et al., Phys. Med. Biol. 33:1433-1442, 1988; which is incorporatedherein by reference.

[0041] This approach may be implemented by applying:${\Delta \quad \mu_{a}} = {\left( {\lambda,t} \right) = {\frac{1}{L_{eff}}{\ln \left\lbrack \frac{I\left( {\lambda,0} \right.}{I\left( {\lambda,t} \right)} \right\rbrack}}}$

[0042] where L_(eff) is the effective optical pathlength from theilluminating point to the light collection point. L_(eff) is given interms of the tissue absorption coefficient (μ_(a)) and tissue scattercoefficient (μ_(s)′) and the source detector separation r in asemi-infinite turbid medium (where the illumination and collectionpoints are at the boundary of the turbid medium) by:$L_{eff} = \frac{3\mu_{s}^{\prime}r^{2}}{2\left( {{r\sqrt{\left( {3\mu_{a}\mu_{s}^{\prime}} \right)}} + 1} \right)}$

[0043] Application of this relationship indicates that for typicalvalues of the near-infrared absorption and reduced scatteringcoefficients of tissues (e.g., μ_(a)=0.1 cm⁻¹, and (μ_(s)′=10 cm⁻¹), thevalue of L_(eff) is about 5.5 cm for r=1 cm.

[0044] A multi-distance measurement scheme may be implemented using datacollected by fiber bundle detectors 22 located at two distances (e.g.,1.0 and 2.0 cm) from the source fiber bundle 20. At thesesource-detector distances, light propagation in tissues occurs accordingto a diffusion regime as per studies previously reported in theliterature (see, e.g., “Effective Source Term in the Diffusion Equationfor Photon Transport in Turbid Media”, by Fantini et al., Appl. Opt. 36:156-163 (1997); incorporated herein by reference). Those knowledgeablein the art will appreciate that empirical methods are available as analternative to the diffusion equation model to describe the spatialdependence of the optical signal. In general, the different sensitivityof the two channels may be accounted for through a preliminarycalibration measurement on a tissue-like sample.

[0045] The present invention further comprises data processing steps forevaluating the amplitude of the respiration-induced absorptionoscillations. One invention embodiment comprises data processing stepsof calculating amplitudes through a Fourier transform, and preferably afast Fourier transform (FFT), of Δμ_(a) at the respiration frequency(blocks 32 and 34 in FIG. 2). The sum of the amplitudes of the FFTΔμ_(a) over the respiratory frequency band yields a measure of theamplitude of the respiration induced absorption oscillations. Thismethod assumes that the Fourier spectrum of Δμ_(a) shows a discernablepeak at the respiratory frequency. The FFT requires multiple respirationcycles to produce a venous saturation reading. Of the order of 10-15respiration cycles are preferred.

[0046] A second invention embodiment comprises data processing steps forcomputing the amplitude of the respiratory induced absorptionoscillations through use of a band pass filter in combination with usinga modeling algorithm. The band pass filter serves the purpose ofisolating the absorption oscillations at the respiratory frequency bysuppressing higher and lower frequency components in Δμ_(a). A preferredmodeling algorithm comprises a sin-wave model fit to each respiratorycycle. The amplitude of the fitted sin wave gives an estimate of theabsorption oscillation amplitude

[0047] The FFT and the band pass filter methods each havecharacteristics that may make one or the other set of data processingsteps preferable over the other under particular circumstances. Themajor advantage of the band pass filter method is that it can achieve avenous saturation reading from each individual respiratory cycle. TheFFT method, on the other hand, requires data from multiple respiratorycycles to produce venous saturation readings. Consequently, the bandpass filter method is particularly effective during transients. The bandpass filter method, on the other hand, is also susceptible to error fromfluctuations in the respiratory frequency and/or from irregularrespiration patterns. The FFT method proves much more robust in theseinstances, as the irregular effects are averaged out over the multiplerespiratory cycles measured.

[0048] With these considerations in mind, care should be taken inchoosing which method to use. If a venous saturation level is to bemeasured that will involve changes induced by exercise, for instance,the band pass filter method may be preferred as it will more clearlyrepresent changing saturation levels. If a subject is to be measured atrest or where venous saturation is otherwise expected to be in arelatively steady state, on the other hand, the FFT method may bepreferred.

[0049] Once the amplitude of the oscillations has been computed,regardless of the method used, the spectrum of the absorptionoscillations at the respiratory frequency must be fit to determine timevarying hemoglobin saturation levels. The present invention fits thespectrum with a linear combination of the extinction spectra ofoxygenated hemoglobin and deoxygenated hemoglobin:[ε_(HbO2)(λ_(i))Δ[HbO₂]^(RESP)+ε_(Hb)(λ_(i))Δ[Hb]^(RESP)], whereε_(HbO2)(λ_(i)) and ε_(Hb)(λ_(i)) are the extinction coefficients ofoxygenated and deoxygenated hemoglobin, respectively. The fittedparameters are the oscillatory concentrations of oxygenated hemoglobin([HbO₂]^(RESP)) and deoxygenated hemoglobin ([Hb]^(RESP)) at therespiratory frequency. The venous hemoglobin saturation is then given bythe expression [HbO₂]^(RESP)/([HbO₂ ]^(RESP)+[Hb]^(RESP)), where thesuperscript “RESP” indicates the respiration origin (block 36).

[0050] The minimization of the sum of the squares of the residuals(i.e., Σ_(i)[Δμ_(a) ^((fit))(λ_(i))−Δμ_(a) ^((resp))(λ_(i))]²) yields alinear system whose solution gives the best fit concentrations ofamplitude of the oscillatory oxy-and deoxy-hemoglobin concentrationsshown below by EQTN. 1. The oxygen saturation of the hemoglobincompartment oscillating synchronously with respiration (SvO₂-NIRS) isthen given by EQTN 2 shown below. $\begin{matrix}{{{{\Delta \left\lbrack {HbO}_{2} \right\rbrack}^{({resp})} = \frac{\begin{matrix}{{\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}}} \right)\left( {\sum\limits_{i}{ɛ_{Hb}^{2}\left( \lambda_{i} \right)}} \right)} -} \\{\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{Hb}\left( \lambda_{i} \right)}}} \right)\left( {\sum\limits_{i}{{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}ɛ_{Hb}\left( \lambda_{i} \right)}} \right)}\end{matrix}}{{\left( {\sum\limits_{i}{ɛ_{{HbO}\quad 2}^{2}\left( \lambda_{i} \right)}} \right)\left( {\sum\limits_{i}{ɛ_{Hb}^{2}\left( \lambda_{i} \right)}} \right)} - \left( {\sum\limits_{i}{{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}{ɛ_{Hb}\left( \lambda_{i} \right)}}} \right)}}{{\Delta \lbrack{Hb}\rbrack}^{({resp})} = \frac{\begin{matrix}{{\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{Hb}\left( \lambda_{i} \right)}}} \right)\left( {\sum\limits_{i}{ɛ_{{HbO}\quad 2}^{2}\left( \lambda_{i} \right)}} \right)} -} \\{\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}}} \right)\left( {\sum\limits_{i}{{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}ɛ_{Hb}\left( \lambda_{i} \right)}} \right)}\end{matrix}}{{\left( {\sum\limits_{i}{ɛ_{{HbO}\quad 2}^{2}\left( \lambda_{i} \right)}} \right)\left( {\sum\limits_{i}{ɛ_{Hb}^{2}\left( \lambda_{i} \right)}} \right)} - \left( {\sum\limits_{i}{{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}{ɛ_{Hb}\left( \lambda_{i} \right)}}} \right)^{2}}}}} & {{EQTN}.\quad 1} \\{{{S_{V}O_{2}} - {NIRS}_{resp}} = {\frac{{\Delta \left\lbrack {HbO}_{2} \right\rbrack}^{({resp})}}{{\Delta \left\lbrack {HbO}_{2} \right\rbrack}^{({resp})} + {\Delta \lbrack{Hb}\rbrack}^{({resp})}} = \frac{\begin{matrix}{{\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}}} \right)\left( {\sum\limits_{i}{ɛ_{Hb}^{2}\left( \lambda_{i} \right)}} \right)} -} \\{\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{Hb}\left( \lambda_{i} \right)}}} \right)\left( {\sum\limits_{i}{{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}ɛ_{Hb}\left( \lambda_{i} \right)}} \right)}\end{matrix}}{\begin{matrix}\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}}} \right) \\{\left( {\sum\limits_{i}{{ɛ_{Hb}\left( \lambda_{i} \right)}\left\lbrack {{ɛ_{Hb}\left( \lambda_{i} \right)} - {ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}} \right\rbrack}} \right) -}\end{matrix}\begin{matrix}\left( {\sum\limits_{i}{\Delta \quad {\mu_{a}^{({resp})}\left( \lambda_{i} \right)}{ɛ_{Hb}\left( \lambda_{i} \right)}}} \right) \\\left( {\sum\limits_{i}{{ɛ_{{HbO}\quad 2}\left( \lambda_{i} \right)}\left\lbrack {{ɛ_{Hb}\left( \lambda_{i} \right)} - {ɛ_{{HbO}\quad 2}\left( \lambda_{1} \right)}} \right\rbrack}} \right)\end{matrix}}}} & {{EQTN}.\quad 2}\end{matrix}$

[0051] It is noted that for the determination of SvO₂-NIRS_(resp),L_(eff) is only required to within a wavelength-independent factor. Infact, the above equation shows that a common, wavelength independentmultiplicative factor in Δμ_(a)(λ_(i)) cancels out in the expression forSvO₂-NIRS_(resp). By contrast, the wavelength dependence of L_(eff) isimportant for the measurement of venous saturation with the presentmethod. Accordingly, L_(eff) is preferably measured at each wavelengthusing the multi-distance, frequency-domain technique.

[0052] It is also noted that in order for the calculation of this methodembodiment to be correct, it is required that: (1) the oscillations ofμ_(a) at the respiratory frequency can be reliably attributed tohemoglobin (and not, for instance, to motion artifacts); (2) thehemoglobin-concentration fluctuations result from the volume oscillationof a hemoglobin compartment rather than from periodic fluctuations inthe blood flow; and (3) the fluctuating hemoglobin compartmentresponsible for the measured Δμ_(a) is the venous compartment.Embodiments of the present invention provide steps for addressing thesethree requirements.

[0053] It will be appreciated that steps taken to address these threepoints may generally be referred to as “verification steps”. That is,insuring that measurements meet with any or all of the threerequirements of points (1)-(3) may be considered a verification of theaccuracy of the measurements. Further, it will be appreciated that theseverification steps will be of utility for other method embodiments thatmay comprise different particular calculational methods. Indeed, a levelof uncertainty exists with virtually any method for optical venoussaturation measurement. For example, it is known that respiratory sinusarrhythmia, which is present in all healthy human subjects, inducesfluctuations in the arterial compartment at the respiratory frequency.These fluctuations can confound the isolation of the venous saturationlevel at the respiration frequency. Thus methods that rely onmeasurement of respiratory oscillations have an uncertainty associatedwith them regarding potential arterial compartment contribution.

[0054]FIG. 4 is a flowchart illustrating some of the variousverification steps contemplated by methods of the present invention toinsure measurement accuracy. It will be appreciated that the flowchartof FIG. 4 presents these steps in series for purposes of convenienceonly, and that different method embodiments may comprise using anyparticular order or number of verification steps. By way of example,different method embodiments may comprise using only one of theseverification steps.

[0055] With reference now drawn to FIG. 4, one verification step 102comprises requiring that the hemoglobin spectrum closely fits or matchesthe absorption data relatively well. It will be appreciated that such adetermination will be of particular use in addressing point (1) above,i.e. that measured oscillations result from hemoglobin. Thoseknowledgeable in the art will also appreciate that there are a varietyof methods for determining whether two data sets “closely fit or matchone another”. For example, a visual comparison between output data canbe indicative of a close match. Also, a variety of calculationalcomparison methods are available. A comparison can be made between theresiduals and the experimental error in the absorption data. By way of amore particular example, one example of this step 102 may compriserequiring that the absolute value of the relative residuals be at mosttwice the experimental percent error in absorption data. A secondexample step 102 may comprise analyzing the standard deviation of datacollected over a plurality of points, and discarding samples having anunacceptably high standard deviation. For example, absorption valuesobtained over several hundred successive Fourier transformation datapoints may be analyzed to estimate the error. Data having a standarddeviation error greater than 15% or some other suitable level may bediscarded.

[0056] An additional verification step 104 comprises verifying that theabsorption oscillations at different wavelengths are in phase with oneanother. Preferably, this should be done for each pair of the pluralityof wavelengths measured. In-phase measurements confirm that theoscillations are a result of volume fluctuations and not flowfluctuations. That is, if absorption oscillations are not in phase, thenthe oxygenated and deoxygenated hemoglobin oscillations are not inphase, which suggests that what is being measured results from a bloodflow fluctuation due to an increased rate of blood inflow of oxygenatedhemoglobin and outflow of deoxygenated hemoglobin. Oscillations inphase, on the other hand, suggest that what is being measured is avolume fluctuation as opposed to a flow fluctuation. As a result,in-phase absorption oscillations confirm that the oxygen saturation of atime-varying hemoglobin compartment is being measured.

[0057] Still another verification step 106 comprises verifying that theamplitudes of the measured oxygenated and deoxygenated hemoglobinconcentration peaks at the heartbeat frequency are less than those atthe respiratory frequency. This provides an objective verification toinsure that the venous compartment dominates measurements.

[0058] Yet another verification step 108 of a method embodimentcomprises verifying that the spectrometer probe is placed closelyadjacent, and preferably directly over, a visible vein. Experimentsconducted indicate that when the probe is placed directly over a visiblevein it is substantially more likely that the measured oscillationsresult from venous contributions than when the probe is placed notadjacent to a vein. It is noted that in computer program productembodiments of the present invention, this step may be carried outthrough a query of the user, or through detection means.

[0059]FIG. 5 is useful in illustrating results of this verificationstep. FIG. 5(a) illustrates oscillation measurements made using a methodembodiment of the present invention on the calf of a human subject, withthe probe placed over a visible vein. In particular, FIG. 5(a)illustrates a summary of measurement of deoxygenated hemoglobin (Hb) andoxygenated hemoglobin (HbO₂), with the oscillations appearing in phasewith the respiratory cycle. Without moving the probe, a venous occlusionwas placed on the thigh of the subject to suppress venous compartmentvolume fluctuations. Resulting data are summarized in FIG. 5(b),confirming that the venous compartment is dominating measurements, asthe oscillations of oxygenated and deoxygenated hemoglobin aresuppressed during venous occlusion even though no blockage of thearteries occurs. It is noted that FIGS. 5(a) and (b) are data plotsrepresentative of data following processing with one of the amplitudecalculation methods of the invention, e.g., after application of a bandpass filter and fitting with a sin wave algorithm.

[0060] It has been discovered that the novel method of the invention,preferably comprising one or more of the verification steps, allows foraccurate measurements of venous oxygenation saturation levels to be madewithout requiring mechanical ventilation, venous occlusions, or otheradditional required elements of methods of the prior art. It is notedthat it may be desirable when using a method of the invention with ahuman subject to use a metronome or other patient frequency guide toregulate breathing frequency.

[0061] The novel methods of the present invention were tested on anumber of subjects including piglets and humans. In addition to themethod of the present invention, for comparison purposes the testsubjects were likewise subjected to known venous saturation measurementmethods of the prior art using venous occlusions and/or invasivemeasurements. Generally these tests indicated very good agreementbetween venous saturation levels determined through methods of thepresent invention as compared to results obtained through methods of theprior art.

[0062] The tests' results also provide some useful guidance regardingpractice of various aspects of the present invention. For instance, thetests offer a comparison between use of FFT and band pass filter dataprocessing steps. Consistent with the discussion regarding the twomethods made herein above, it was generally observed that the band passfilter method has significant advantages in terms of its ability todetect transients and/or changing saturation levels. Because it isaveraged over multiple respiration cycles, the FFT method was found tobe fairly robust during steady state conditions.

[0063] Also, the tests highlighted the importance of steps of theinvention related to verifying that the venous compartment contributionsdominated the respiratory oscillations being measured. As discussedabove, this can be accomplished through one or more of a number ofpotential verification steps, including for example by placing the probeon top of a visible vein, by verifying that the respiratory oxygenatedand deoxygenated hemoglobin oscillations are in phase, and by verifyingthat the oxygenated and deoxygenated oscillations have a greaterintegrated amplitude at the respiratory frequency than at the heartbeatfrequency. These verification steps can be used to greatly increase theconfidence level in optical venous saturation measurements.

[0064] Those knowledgeable in the art will also appreciate that thepresent invention is well suited for practice in the form of a computerprogram product. Accordingly, it will be appreciated that additionalembodiments of the invention comprise computer program products. Thesecomputer program product embodiments comprise computer executableinstructions stored in a computer readable medium that when executedcause the computer to perform prescribed actions. Generally, thecomputer program product embodiments of the invention cause a computerto perform the steps of method embodiments of the invention.Accordingly, it will be appreciated that discussion made hereinregarding method embodiments may likewise apply to computer programproduct embodiments.

[0065] Examples of computer readable mediums comprise memories such asmagnetic or optical disks and the like, electronic circuitry, embeddedcircuitry, RAM, ROM, EPROM, SDRAM, and the like. Also, it will beappreciated that the term “computer” as used herein is intended tobroadly refer to processor based devices capable of executinginstructions. Accordingly, examples of “computers” as used herein maycomprise, but are not limited to, personal computers, hand held devices,mainframe computers, processor based instrumentation such asspectrometers, processor based data acquisition and control devices, andthe like.

[0066] It will also be appreciated that some of the measurement and dataprocessing aspects of the present invention are consistent with thosedisclosed in U.S. Pat. No. 6,216,021 issued Apr. 10, 2001 toFranceschini et al. (the '021 patent), which has been incorporatedherein by reference. While description has been given herein to enablepractice of the known best modes of methods of the present invention,additional useful information regarding some consistent aspects, such asmeasurement and data processing steps, of the present invention may behad through reference to the '021 patent.

[0067] From the foregoing description, it should be understood thatimproved methods have been shown and described which have many desirableattributes and advantages. For example, a time-resolved measurementapproach is presented to non-invasively measure the absolute value oftime-varying hemoglobin venous compartment saturation without requiringmechanical ventilation or venous occlusion. Other alterations andmodifications will be apparent to those skilled in the art. Accordingly,the scope of the invention is not limited to the specific embodimentsused to illustrate the principles of the invention. Instead, the scopeof the invention is properly determined by reference to the appendedclaims and any legal equivalents thereof.

What is claimed is:
 1. A method for measuring the venous oxygensaturation comprising the steps of: illuminating an area of tissue of asubject with intensity modulated light at at least two wavelengths, saidsubject being free from mechanical ventilation devices and free fromvenous perturbations sensing diffusely reflected light caused by saidillumination to acquire time resolved measurement data; and processingsaid time resolved measurement data to determine the absolute saturationof the venous compartment, said processing comprising at leastdetermining an amplitude of respiration induced absorption oscillations.2. A method for measuring the venous oxygen saturation as in claim 1wherein: the processing step comprises determining at least amplitudesfor oxygenated and deoxygenated hemoglobin oscillations at therespiratory frequency.
 3. A method for measuring the venous oxygensaturation as in claim 1 wherein: the step of sensing diffuselyreflected light comprises sensing diffusely reflected light at aplurality of wavelengths; and wherein the processing step comprisesdetermining an amplitude of respiration induced absorption oscillationsat each of said plurality of wavelengths.
 4. A method for measuring thevenous oxygen saturation as in claim 3 wherein said plurality ofwavelengths comprises at least eight.
 5. A method for measuring thevenous oxygen saturation as in claim 1 wherein: the processing stepcomprises quantifying said amplitude of absorption oscillations at saidrespiratory frequency using a fast Fourier transformation.
 6. A methodfor measuring the venous oxygen saturation as in claim 5 wherein themethod comprises acquiring time resolved measurement data over at least10 respiratory cycles.
 7. A method for measuring the venous oxygensaturation as in claim 1 wherein: the processing step comprisesquantifying said amplitude of absorption oscillations at saidrespiratory frequency by using a band pass filter to isolate saidabsorption oscillations at said respiratory frequency and subsequentlyapplying a modeling algorithm to said absorption oscillations over eachrespiratory cycle.
 8. A method for measuring the venous oxygensaturation as in claim 7 wherein said modeling algorithm comprises a sinwave fitting algorithm.
 9. A method for measuring the venous oxygensaturation as in claim 1 wherein the method further comprises the stepof verifying that said absorption oscillations at said at least twowavelengths occur in phase at said respiratory frequency.
 10. A methodfor measuring the venous oxygen saturation as in claim 1 wherein themethod further comprises placing a probe on said area of tissue, saidprobe for generating said intensity modulated light for illuminatingsaid area of tissue, and wherein said probe is placed directly over avisible vein
 11. A method for measuring the venous oxygen saturation asin claim 1 wherein the step of processing said time resolved measurementdata comprises determining an amplitude of absorption oscillations atthe heartbeat frequency and verifying that the amplitude of saidheartbeat frequency absorption oscillations is less than the amplitudeof said respiration induced absorption oscillations.
 12. A method formeasuring the venous oxygen saturation as in claim 1 wherein the step ofprocessing said time resolved measurement data comprises determiningabsorption data, and wherein the method further comprises verifying thata hemoglobin spectrum closely agrees with said absorption data.
 13. Amethod for measuring the venous oxygen saturation as in claim 1 whereinsaid steps of illuminating and sensing are conducted on a common side ofsaid area of tissue.
 14. A method for measuring the venous oxygensaturation as in claim 1 wherein said processing step comprises fittingsaid amplitude of respiration induced absorption oscillations with alinear combination of extinction spectra of oxygenated and deoxygenatedhemoglobin.
 15. A method for measuring the venous oxygen saturation asin claim 1 wherein said processing step comprises determining aneffective optical path length between an illumination source and anilluminated point in said illuminated area of tissue, said effectiveoptical path length being used to determine said amplitude ofrespiration induced absorption oscillations.
 16. A method for measuringthe venous oxygen saturation as in claim 1 wherein said processing stepcomprises determining a tissue scattering coefficient and a tissueabsorption coefficient, said scattering coefficient and said absorptioncoefficient being used to determine said amplitude of respirationinduced absorption oscillations.
 17. A method for measuring the venousoxygen saturation as in claim 1 wherein the time resolved dataprocessing step comprises: determining a reduced scattering coefficientand an absorption coefficient for said tissue; and using said reducedscattering coefficient and said absorption coefficient to determine aneffective optical path length between an illumination source and anilluminated point in said illuminated area of tissue, said effectiveoptical path length being used to determine said amplitude ofrespiration induced absorption oscillations.
 18. A method for measuringa venous oxygen saturation comprising the steps of: illuminating an areaof tissue of a subject with intensity modulated light in the visible andnear infrared spectral range at at least two wavelengths on a commonside of said area of tissue; sensing diffusely reflected light caused bysaid illumination to acquire time resolved measurement data for each ofsaid at least two wavelengths; and processing said time resolvedmeasurement data at each of said at least two wavelengths to determinethe absolute saturation of the venous compartment, said processingcomprising determining a reduced scattering coefficient and anoscillation absorption coefficient, quantifying an amplitude ofrespiration induced absorption oscillations using said reducedscattering coefficient and said oscillation absorption coefficient andusing one of either a Fourier transformation or a band pass filter, andfitting said amplitude of respiration induced absorption oscillationswith a linear combination of extinction spectra of oxygenated anddeoxygenated hemoglobin.
 19. A method for measuring a venous oxygensaturation level as in claim 18 wherein said subject being free frommechanical ventilation devices and venous perturbation.
 20. A method formeasuring the venous oxygen saturation comprising the steps of:illuminating an area of tissue with a probe generating intensitymodulated light at a plurality of wavelengths; sensing diffuselyreflected light caused by said illumination to acquire time resolvedmeasurement data; processing said time resolved measurement data todetermine the absolute saturation of the venous compartment, saidprocessing comprising at least determining an amplitude of respirationinduced absorption oscillations; and verifying that said measurementdata is accurate.
 21. A method for measuring the venous oxygensaturation as in claim 20 wherein the step of verifying that saidmeasurement data is accurate comprises measuring time resolvedabsorption oscillation data at said plurality of wavelengths andverifying that said oscillations are in phase at the respiratoryfrequency.
 22. A method for measuring the venous oxygen saturation as inclaim 19 wherein the step of verifying that said measurement data isaccurate comprises verifying that said probe is located directly over avisible vein.
 23. A method for measuring the venous oxygen saturation asin claim 20 wherein the step of verifying that said measurement data isaccurate comprises determining amplitudes of oxygenated and deoxygenatedhemoglobin concentration oscillations at a heartbeat frequency and at arespiratory frequency, and verifying that said amplitudes at saidheartbeat frequency are less than said amplitudes at said respiratoryfrequency.
 24. A method for measuring the venous oxygen saturation as inclaim 20 wherein the step of processing said time resolved datacomprises determining absorption data, and wherein the step of verifyingthat said measurement data is accurate comprises verifying that ahemoglobin spectrum closely agrees with said absorption data.
 25. Acomputer program product for causing a computer to measure the venousoxygen saturation, the program product comprising computer executableinstructions stored on a computer readable medium, the programinstructions when executed causing the computer to: illuminate an areaof tissue using a probe generating intensity modulated light at aplurality of wavelengths; sense diffusely reflected light caused by saidillumination to acquire time resolved measurement data; process saidtime resolved measurement data to determine the absolute saturation ofthe venous compartment, said processing comprising at least determiningan amplitude of respiration induced absorption oscillations; and verifythat said time resolved measurement data represents venous compartmentdata.
 26. A computer program product as in claim 25, wherein the programinstructions when executed cause the computer to verify that said probeis properly located by causing the computer to perform one or more ofthe steps from the group of steps consisting of: measuring time resolvedabsorption oscillation data at said plurality of wavelengths andverifying that said oscillations are in phase at the respiratoryfrequency, and determining amplitudes of oxygenated and deoxygenatedhemoglobin concentration oscillations at a heartbeat frequency and at arespiratory frequency and verifying that said amplitudes at saidheartbeat frequency are less than said amplitudes at said respiratoryfrequency.
 27. A computer program product as in claim 25 wherein saidsubject being free from mechanical ventilation devices and venousperturbation.